Mapping the research landscape of artificial intelligence for knowledge discovery in innovation research
DOI:
https://doi.org/10.47989/ir31152435Abstract
Introduction. Artificial intelligence (AI) is increasingly vital for knowledge discovery within innovation research (IR) and the science of science (SoS), yet its specific research landscape lacks systematic mapping. This study addresses this gap by providing a comprehensive bibliometric overview of AI's application for knowledge discovery in innovation research, aiming to structure the field and identify key trends.
Method. A bibliometric analysis was performed on 1,094 articles and reviews published between 2010 and 2024, retrieved from the Web of Science Core Collection. Data processing and visualisation employed VOSviewer and Bibliometrix.
Analysis. Descriptive statistics quantified publication growth and collaboration patterns. Network analyses mapped thematic structures, using keyword co-occurrence; identified intellectual foundations, through co-citation networks; and visualised current research frontiers through bibliometric coupling.
Results. Findings indicate exponential publication growth and high international collaboration, dominated by China and the USA. Key thematic clusters focus on AI methodologies (machine learning (ML), deep learning (DL), natural language processing (NLP)), innovation contexts (patent analysis, technology trends), and integrated science of science methods (bibliometrics, scientometrics). Intellectual foundations derive strongly from computer vision, sequence/topic modelling, and bibliometric tools.
Conclusion. This mapping structures the field, highlighting AI's profound integration as both a transformative tool for innovation analysis and an object of study within the science of science framework itself. It underscores the field's dynamism and provides a basis for future research on AI's impact and responsible application.
References
Agrawal, A., McHale, J., & Oettl, A. (2024). Artificial intelligence and scientific discovery: a model of prioritized search. Research Policy, 53(5), 104989. https://doi.org/10.1016/j.respol.2024.104989
Aria, M., & Cuccurullo, C. (2017). Bibliometrix: an R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Ayinaddis, S. G. (2025). Artificial intelligence adoption dynamics and knowledge in SMEs and large firms: a systematic review and bibliometric analysis. Journal of Innovation & Knowledge, 10(3), 100682. https://doi.org/10.1016/j.jik.2025.100682
Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022. https://doi.org/10.5555/944919.944937
Bogers, M., Chesbrough, H., & Moedas, C. (2018). Open innovation: Research, practices, and policies. California Management Review, 60(2), 5–16. https://doi.org/10.1177/0008125617745086
Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: an introduction to co-word analysis. Social Science Information, 22(2), 191–235. https://doi.org/10.1177/053901883022002003
Chavarriaga, R., Sagha, H., Calatroni, A., Digumarti, S. T., Tröster, G., Millán, J. del R., & Roggen, D. (2013). The opportunity challenge: A benchmark database for on-body sensor-based activity recognition. Pattern Recognition Letters, 34(15), 2033–2042. https://doi.org/10.1016/j.patrec.2012.12.014
Chellappa, R., Theodoridis, S., & van Schaik, A. (2021). Advances in machine learning and deep neural networks. Proceedings of the IEEE, 109(5), 607–611. https://doi.org/10.1109/jproc.2021.3072172
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255). https://doi.org/10.1109/cvpr.2009.5206848
Dessimoz, C., & Thomas, P. D. (2024). AI and the democratization of knowledge. Scientific Data, 11, 268. https://doi.org/10.1038/s41597-024-03099-1
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., & Barabási, A.-L. (2018). Science of science. Science, 359(6379), eaao0185. https://doi.org/10.1126/science.aao0185
Gama, F., & Magistretti, S. (2025). Artificial intelligence in innovation management: A review of innovation capabilities and a taxonomy of AI applications. Journal of Product Innovation Management, 42(1), 76–111. https://doi.org/10.1111/jpim.12698
Geng, Y., Peveler, W. J., & Rotello, V. M. (2019). Array‐based “Chemical Nose” sensing in diagnostics and drug discovery. Angewandte Chemie, 58(16), 5190–5200. https://doi.org/10.1002/anie.201809607
Gu, Y., Qian, Z. (S.) & Chen, F. (2016). From twitter to detector: Real-time traffic incident detection using social media data. Transportation Research Part C: Emerging Technologies, 67, 321–342. https://doi.org/10.1016/j.trc.2016.02.011
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. https://doi.org/10.1109/cvpr.2016.90
Howoldt, D. (2024). Characterising innovation policy mixes in innovation systems. Research Policy, 53(2), 104902. https://doi.org/10.1016/j.respol.2023.104902
Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7132–7141). https://doi.org/10.1109/cvpr.2018.00745
Jarrahi, M. H., Askay, D., Eshraghi, A., & Smith, P. (2022). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87–99. https://doi.org/10.1016/j.bushor.2022.03.002
Javed, A. R., Shahzad, F., Rehman, S. ur, Zikria, Y. B., Razzak, I., Jalil, Z., & Xu, G. (2022). Future smart cities requirements, emerging technologies, applications, challenges, and future aspects. Cities, 129(1), 103794. https://doi.org/10.1016/j.cities.2022.103794
Katz, J. Sylvan., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18. https://doi.org/10.1016/s0048-7333(96)00917-1
Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10–25. https://doi.org/10.1002/asi.5090140103
Khurana, D., Koli, A., Khatter, K., & Singh, S. (2022). Natural language processing: State of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4
Lê, J. K., & Schmid, T. (2020). The practice of innovating research methods. Organizational Research Methods, 25(2), 308–336. https://doi.org/10.1177/1094428120935498
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Li, L., Li, Y., & Li, Z. (2013). Efficient missing data imputing for traffic flow by considering temporal and spatial dependence. Transportation Research Part C: Emerging Technologies, 34, 108–120. https://doi.org/10.1016/j.trc.2013.05.008
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., & Pietikäinen, M. (2018). From BoW to CNN: Two decades of texture representation for texture classification. International Journal of Computer Vision, 127(1), 74–109. https://doi.org/10.1007/s11263-018-1125-z
Malekloo, A., Ozer, E., AlHamaydeh, M., & Girolami, M. (2021). Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights. Structural Health Monitoring, 21(4), 147592172110368. https://doi.org/10.1177/14759217211036880
Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials, 20(4), 2595–2621. https://doi.org/10.1109/COMST.2018.2846401
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023a). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
Mariani, M. M., Machado, I. & Nambisan, S. (2023b). Types of innovation and artificial intelligence: a systematic quantitative literature review and research agenda. Journal of Business Research, 155, 113364https://doi.org/10.1016/j.jbusres.2022.113364
Marino, D., Gil Lafuente, J., & Tebala, D. (2023). Innovations and development of artificial intelligence in Europe: Some empirical evidences. European Journal of Management and Business Economics, 32(5), 620–636. https://doi.org/10.1108/ejmbe-03-2023-0085
Maruccia, Y., Solazzo, G., Del Vecchio, P., & Passiante, G. (2020). Evidence from network analysis application to innovation systems and quintuple helix. Technological Forecasting and Social Change, 161, 120306. https://doi.org/10.1016/j.techfore.2020.120306
Moon, D., & Ahn, S. (2025). Metrics and algorithms for identifying and mitigating bias in AI design: a counterfactual fairness approach. IEEE Access, 13, 59118–59129. https://doi.org/10.1109/access.2025.3556082
Nguyen, B. H., Xue, B., & Zhang, M. (2020). A survey on swarm intelligence approaches to feature selection in data mining. Swarm and Evolutionary Computation, 54, 100663. https://doi.org/10.1016/j.swevo.2020.100663
Radu, R. (2021). Steering the governance of artificial intelligence: National strategies in perspective. Policy and Society, 40(2), 178–193. https://doi.org/10.1080/14494035.2021.1929728
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention: MICCAI 2015 (Lecture Notes in Computer Science, Vol. 9351; pp. 234–241). https://doi.org/10.1007/978-3-319-24574-4_28
Secundo, G., Spilotro, C., Gast, J., & Corvello, V. (2025). The transformative power of artificial intelligence within innovation ecosystems: A review and a conceptual framework. Review of Managerial Science, 19, 2697–2728. https://doi.org/10.1007/s11846-024-00828-z
Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406
Sun, Y., & Latora, V. (2020). The evolution of knowledge within and across fields in modern physics. Scientific Reports, 10, 12097. https://doi.org/10.1038/s41598-020-68774-w
van der Maaten, L., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. https://www.jmlr.org/papers/v9/vandermaaten08a.html (Archived in the Internet Archive at https://web.archive.org/web/20250124104847/https://jmlr.org/papers/v9/vandermaaten08a.html
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 6000–6010). https://dl.acm.org/doi/10.5555/3295222.3295349
Waltman, L. (2016). A review of the literature on citation impact indicators. Journal of Informetrics, 10(2), 365–391. https://doi.org/10.1016/j.joi.2016.02.007
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., Anandkumar, A., Bergen, K., Gomes, C. P., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T.-Y., Manrai, A., & Marks, D. (2023). Scientific discovery in the age of artificial intelligence. Nature, 620, 47–60. https://doi.org/10.1038/s41586-023-06221-2
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., Han, M., & Zhao, X. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027–1047. https://doi.org/10.1016/j.rser.2017.09.108
Wu, D., Xu, Y., & Lu, B.-L. (2022). Transfer learning for EEG-based brain–computer interfaces: A review of progress made since 2016. IEEE Transactions on Cognitive and Developmental Systems, 14(1), 4–19. https://doi.org/10.1109/TCDS.2020.3007453
Yuan, F., Zhang, Z., & Fang, Z. (2023). An effective CNN and transformer complementary network for medical image segmentation. Pattern Recognition, 136, 109228. https://doi.org/10.1016/j.patcog.2022.109228
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Yanyi Wu
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
